A Global Multilayer Cloud Identification with POLDER/PARASOL

Desmons, Marine; Ferlay, Nicolas; Parol, Frédéric; Riédi, J.; Thieuleux, F.

doi:10.1175/jamc-d-16-0159.1

Cited by 19 publications

(17 citation statements)

References 49 publications

(55 reference statements)

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“…The measurement noise was modelled as an additive Gaussian random variable with a zero mean and a standard devia-tion of 1 % for reflectance, 2 % for polarized reflectance and 0.7 % for DoLP. As done in previous work (Di Noia et al, 2015Noia et al, , 2017, such noise was added to the synthetic measurements during the training phase as a form of regularization, as explained in Bishop (1995). The neural network architecture was chosen based on results inherited from previous work and, for all the four networks, consists of three hidden layers with 40 neurons each.…”

Section: Training Set Generationmentioning

confidence: 99%

“…Among the attractive features of NN-based retrieval schemes are their high speed and their modest memory demand (at least after the training phase is completed), which make them suitable for processing large amounts of measurements in very little time. Furthermore, NN retrievals have sometimes been shown to be more accurate than lookup-table (LUT) retrievals with reasonably sized LUTs (Di Noia et al, 2015Whitburn et al, 2016). Besides the aforementioned advantages, NN-based schemes also have some disadvantages with respect to other methods.…”

Section: Introductionmentioning

confidence: 99%

“…While most of the NN-based cloud retrieval schemes have been developed for single-viewing, multispectral reflectance measurements, the application of neural networks to multiangle observations has been limited so far, with few examples in the closely related task of aerosol retrieval (Han et al, 2006;Di Noia et al, 2015, one in the context of three-dimensional cloud retrievals (Evans et al, 2008) and a recently developed method for cloud retrievals from RSP (Segal-Rozenhaimer et al, 2018). The main challenge posed by multi-angle measurements in the design of neural network retrieval schemes lies in the variability in the combinations of viewing angles at which the measurements are performed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Retrieval of liquid water cloud properties from POLDER-3 measurements using a neural network ensemble approach

et al. 2019

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Abstract. This paper describes a neural network algorithm for the estimation of liquid water cloud optical properties from the Polarization and Directionality of Earth's Reflectances-3 (POLDER-3) instrument aboard the Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) satellite. The algorithm has been trained on synthetic multi-angle, multi-wavelength measurements of reflectance and polarization and has been applied to the processing of 1 year of POLDER-3 data. Comparisons of the retrieved cloud properties with Moderate Resolution Imaging Spectroradiometer (MODIS) products show that the neural network algorithm has a low bias of around 2 in cloud optical thickness (COT) and between 1 and 2 µm in the cloud effective radius. Comparisons with existing POLDER-3 datasets suggest that the proposed scheme may have enhanced capabilities for cloud effective radius retrieval, at least over land. An additional feature of the presented algorithm is that it provides COT and effective radius retrievals at the native POLDER-3 Level 1B pixel level.

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Section: Training Set Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Retrieval of liquid water cloud properties from POLDER-3 measurements using a neural network ensemble approach

et al. 2019

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“…Currently, the only satellites which directly detect 3-D structure information of clouds are CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (Calipso). These platforms use active microwave radar and lidar to accurately detect the cloud boundary and its vertical structure [8][9][10], but with small observation widths. It can only obtain 3-D cloud structure along the track in one observation.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Cloud Structure Reconstruction from the Directional Polarimetric Camera

Ahmad

et al. 2019

Remote Sensing

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Clouds affect radiation transmission through the atmosphere, which impacts the Earth’ s energy balance and climate. Currently, the study of clouds is mostly based on a two-dimensional (2-D) plane rather than a three-dimensional (3-D) space. However, 3-D cloud reconstruction is playing an important role not only in a radiation transmission calculation but in forecasting climate change as well. Currently, the study of clouds is mostly based on 2-D single angle satellite observation data while the importance of a 3-D structure of clouds in atmospheric radiation transmission is ignored. 3-D structure reconstruction would improve the radiation transmission accuracy of the cloudy atmosphere based on multi-angle observations data. Characterizing the 3-D structure of clouds is crucial for an extensive study of this complex intermediate medium in the atmosphere. In addition, it is also a great carrier for visualization of its parameters. Special attributes and the shape of clouds can be clearly illustrated in a 3-D cloud while these are difficult to describe in a 2-D plane. It provides a more intuitive expression for the study of complex cloud systems. In order to reconstruct a 3-D cloud structure, we develop and explore a ray casting algorithm applied to data from the Directional Polarimetric Camera (DPC), which is onboard the GF-5 satellite. In this paper, we use DPC with characteristics of imaging multiple angles of the same target, and characterize observations of clouds from different angles in 3-D space. This feature allows us to reconstruct 3-D clouds from different angles of observations. In terms of verification, we use cloud profile data provided by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) to compare with the results of reconstructed 3-D clouds based on DPC data. This shows that the reconstruction method has good accuracy and effectiveness. This 3-D cloud reconstruction method would lay a scientific reference for future analysis on the role of clouds in the atmosphere and for the construction of 3-D structures of aerosols.

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“…For instance, Baum et al [12] developed a fuzzy-logic based scheme by using the Advanced Very High Resolution Radiometer (AVHRR) measurements, which is based on a domain of 32 × 32 pixels (the pixel is about 1.1 km × 1.1 km) and outputs four types of cloudy scenes, including low-level clouds, middle-level clouds, high-level clouds, and multilayered clouds. Although many efforts [13][14][15][16] have been carried out identifying multilayered clouds based upon passive measurements, their availability is limited. This deficiency is in principle due to the shallow optical depth that visible and infrared radiation can penetrate into clouds.…”

Section: Introductionmentioning

confidence: 99%

Characteristics of Oceanic Warm Cloud Layers within Multilevel Cloud Systems Derived by Satellite Measurements

2019

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Low-level warm clouds are a major component in multilayered cloud systems and they are generally hidden from the top-down view of satellites with passive measurements. This study conducts an investigation on oceanic warm clouds embedded in multilayered structures by using spaceborne radar data with fine vertical resolution. The occurrences of warm cloud overlapping and the geometric features of several kinds of warm cloud layers are examined. It is found that there are three main types of cloud systems that involve warm cloud layers, including warm single layer clouds, cold-warm double layer clouds, and warm-warm double layer clouds. The two types of double layer clouds account for 23% and in the double layer occurrences warm-warm double layer subsets contribute about 13%. The global distribution patterns of these three types differ from each other. Single-layer warm clouds and the lower warm clouds in the cold-warm double layer system they have nearly identical geometric parameters, while the upper and lower layer warm clouds in the warm-warm double layer system are distinct from the previous two forms of warm cloud layers. In contrast to the independence of the two cloud layers in cold-warm double layer system, the two kinds of warm cloud layers in the warm-warm double layer system may be coupled. The distance between the two layers in the warm-warm double layer system is weakly dependent on cloud thickness. Given the upper and lower cloud layer with moderate thickness of around 1 km, the cloudless gap reaches its maximum when exceeding 600 m. The cloudless gap decreases in thickness as the two cloud layers become even thinner or thicker.

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A Global Multilayer Cloud Identification with POLDER/PARASOL

Cited by 19 publications

References 49 publications

Retrieval of liquid water cloud properties from POLDER-3 measurements using a neural network ensemble approach

Retrieval of liquid water cloud properties from POLDER-3 measurements using a neural network ensemble approach

Three-Dimensional Cloud Structure Reconstruction from the Directional Polarimetric Camera

Characteristics of Oceanic Warm Cloud Layers within Multilevel Cloud Systems Derived by Satellite Measurements

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